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New components of the Dictyostelium PKA pathway revealed by Bayesian analysis of expression data
BACKGROUND: Identifying candidate genes in genetic networks is important for understanding regulation and biological function. Large gene expression datasets contain relevant information about genetic networks, but mining the data is not a trivial task. Algorithms that infer Bayesian networks from e...
Autores principales: | , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2873529/ https://www.ncbi.nlm.nih.gov/pubmed/20356373 http://dx.doi.org/10.1186/1471-2105-11-163 |
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author | Parikh, Anup Huang, Eryong Dinh, Christopher Zupan, Blaz Kuspa, Adam Subramanian, Devika Shaulsky, Gad |
author_facet | Parikh, Anup Huang, Eryong Dinh, Christopher Zupan, Blaz Kuspa, Adam Subramanian, Devika Shaulsky, Gad |
author_sort | Parikh, Anup |
collection | PubMed |
description | BACKGROUND: Identifying candidate genes in genetic networks is important for understanding regulation and biological function. Large gene expression datasets contain relevant information about genetic networks, but mining the data is not a trivial task. Algorithms that infer Bayesian networks from expression data are powerful tools for learning complex genetic networks, since they can incorporate prior knowledge and uncover higher-order dependencies among genes. However, these algorithms are computationally demanding, so novel techniques that allow targeted exploration for discovering new members of known pathways are essential. RESULTS: Here we describe a Bayesian network approach that addresses a specific network within a large dataset to discover new components. Our algorithm draws individual genes from a large gene-expression repository, and ranks them as potential members of a known pathway. We apply this method to discover new components of the cAMP-dependent protein kinase (PKA) pathway, a central regulator of Dictyostelium discoideum development. The PKA network is well studied in D. discoideum but the transcriptional networks that regulate PKA activity and the transcriptional outcomes of PKA function are largely unknown. Most of the genes highly ranked by our method encode either known components of the PKA pathway or are good candidates. We tested 5 uncharacterized highly ranked genes by creating mutant strains and identified a candidate cAMP-response element-binding protein, yet undiscovered in D. discoideum, and a histidine kinase, a candidate upstream regulator of PKA activity. CONCLUSIONS: The single-gene expansion method is useful in identifying new components of known pathways. The method takes advantage of the Bayesian framework to incorporate prior biological knowledge and discovers higher-order dependencies among genes while greatly reducing the computational resources required to process high-throughput datasets. |
format | Text |
id | pubmed-2873529 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-28735292010-05-20 New components of the Dictyostelium PKA pathway revealed by Bayesian analysis of expression data Parikh, Anup Huang, Eryong Dinh, Christopher Zupan, Blaz Kuspa, Adam Subramanian, Devika Shaulsky, Gad BMC Bioinformatics Research article BACKGROUND: Identifying candidate genes in genetic networks is important for understanding regulation and biological function. Large gene expression datasets contain relevant information about genetic networks, but mining the data is not a trivial task. Algorithms that infer Bayesian networks from expression data are powerful tools for learning complex genetic networks, since they can incorporate prior knowledge and uncover higher-order dependencies among genes. However, these algorithms are computationally demanding, so novel techniques that allow targeted exploration for discovering new members of known pathways are essential. RESULTS: Here we describe a Bayesian network approach that addresses a specific network within a large dataset to discover new components. Our algorithm draws individual genes from a large gene-expression repository, and ranks them as potential members of a known pathway. We apply this method to discover new components of the cAMP-dependent protein kinase (PKA) pathway, a central regulator of Dictyostelium discoideum development. The PKA network is well studied in D. discoideum but the transcriptional networks that regulate PKA activity and the transcriptional outcomes of PKA function are largely unknown. Most of the genes highly ranked by our method encode either known components of the PKA pathway or are good candidates. We tested 5 uncharacterized highly ranked genes by creating mutant strains and identified a candidate cAMP-response element-binding protein, yet undiscovered in D. discoideum, and a histidine kinase, a candidate upstream regulator of PKA activity. CONCLUSIONS: The single-gene expansion method is useful in identifying new components of known pathways. The method takes advantage of the Bayesian framework to incorporate prior biological knowledge and discovers higher-order dependencies among genes while greatly reducing the computational resources required to process high-throughput datasets. BioMed Central 2010-03-31 /pmc/articles/PMC2873529/ /pubmed/20356373 http://dx.doi.org/10.1186/1471-2105-11-163 Text en Copyright ©2010 Parikh et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research article Parikh, Anup Huang, Eryong Dinh, Christopher Zupan, Blaz Kuspa, Adam Subramanian, Devika Shaulsky, Gad New components of the Dictyostelium PKA pathway revealed by Bayesian analysis of expression data |
title | New components of the Dictyostelium PKA pathway revealed by Bayesian analysis of expression data |
title_full | New components of the Dictyostelium PKA pathway revealed by Bayesian analysis of expression data |
title_fullStr | New components of the Dictyostelium PKA pathway revealed by Bayesian analysis of expression data |
title_full_unstemmed | New components of the Dictyostelium PKA pathway revealed by Bayesian analysis of expression data |
title_short | New components of the Dictyostelium PKA pathway revealed by Bayesian analysis of expression data |
title_sort | new components of the dictyostelium pka pathway revealed by bayesian analysis of expression data |
topic | Research article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2873529/ https://www.ncbi.nlm.nih.gov/pubmed/20356373 http://dx.doi.org/10.1186/1471-2105-11-163 |
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